Well, They Are At It Again

[ UPDATE 12 Feb 2010: Well, I found a CRU letter that describes the update process (in comments below) and it’s not pretty. It looks like there is no particular “ready date” for the GHCN data set. While the data are supposed to be ready a few days after the end of the month (the first distribution of monthly data between national meteorology departments is supposed to happen the 4th of the month) the process as described by Phil Jones in one of the ClimateGate emails is far more ersatz and has no specific bounds on when data are to be thought ready enough to use. In particular there is a second distribution that is supposed to happen between the 18th and the 20th of the month (implied as a quality update) that might well have entirely missing records included. So it looks like you never know when the data are ‘ready’. It isn’t when the data set appears, nor even when it is updated, nor even the end of the month. So at this point we’ll have to treat these as “missing in action” lists rather than KIA. At least for another week or two. Also, Dallas Fort Worth has been found in the “failed QA file”. It would seem that rather than having a record with a -9999 missing data flag, the record is simply dropped until such month as there is a valid datum, then the record comes back, but with the -9999 flag for the failed month. ]

Don’t know what to make of this list yet, other than it directly ‘gives the lie’ to the assertion that thermometer ‘drops’ were / are entirely an artifact of GHCN being a creation at a historical moment in time (i.e. made in 1990’s era so that’s why they drop out then in The Great Dying of Thermometers – which itself ignores The Lesser Dying in 2006).

It also shows that the excuse of things being dropped for not electronically reporting is pretty much a lie, too. I note that Dallas Fort Worth Airport is on this list and I’m pretty sure they have electronic reporting… From the NASA / GISS web site, as confirmation:

(*) Dallas-Fort W 32.9 N 97.0 W 425722590000 4,037,000 1947 – 2009

Note the end date of 2009.

And Strasbourg airport is on the list too, so it’s not just an America thing…

I’ve not examined this list for any patterns, nor re-done any of the prior “by latitude” and “by altitude” reports to see what the changes do to the world. For now, it’s just another “Dig Here” list. (Though a casual look at the altitude field shows a fair number of 1000m and 2000m stations died.)

Oh, and the list is also confirmation that the extraordinary hatred of thermometers shown by the managers of GHCN continues, unabated. Particular emphasis seems to have landed on Africa (already poorly covered) and Asia, with a modest effort to eradicate more of South America. By comparison, Europe is only slightly mauled…

But once you decided that you can just make up any missing data, then who needs to actually read the thermometers any more?

Ok, enough of my complaint. Here is the list. If anyone notices anything interesting about their part of the world, feel free to let us all know. Remember that the StationID (that first field) is structured as 1 digit of continent then 2 that tell the particular country, then 8 for the particular station and substation. So records that start with 1 are Africa, 2 Asia, 3 South America, 4 North America, 5 Pacific, 6 Europe, 7 Antarctica, 8 Ships at sea (a very few geographic spots with few records as a ship happens by and reports). I will break up the list into groups by continent, but notice that there are no “8” stations on the list and only one from Antarctica. Oh, and there were 2 stations added, so I’ll list them here at the top:

Off the right hand edge of the table (not visible on some browsers) are some of the technical fields, like the A flag for “Airstation” (where you often see 1x-9 for a rural non-airport and 1A-9 for a “rural” airport. The A is airport while x is not.

You can shrink the font size or just look at the page source if you wish to see the rest of the record. The only ‘interesting bit’ other than the A flag is the imagination applied to the ‘terrain type’; where what used to be in a surrounding region when a map was made ages ago, is what is asserted to be present today (where Dallas Fort Worth Airport is described as warm fields and woods… in the midst of one of the more extended urban areas on the planet… ). If folks really care, I’ll put the same data in as ‘ragged right’ so you can see it easily.

Oh, and those first two numbers after the name are LAT and LON followed by reported elevation and elevation from a map grid. Then the Urban Suburban Rural flag. Also visible ought to be population in thousands and then the start of a block of codes (that includes the airstation flag). A -9 population is rural.

41 Responses to 2010 Thermometer Langoliers Hit List

And, I’m now pretty sure it goes without saying, that folks are implicitly encouraged to do the time-consuming work of making graphical presentations of the data.

I’ve come to understand that the ordinarily labor-intensive effort to make plots , at present, involves invisible extra steps for the Cheif at this juncture. I think he’s fully aware that plots would greatly enhance the value of all the unique work he did to make the data available, so no need to pile on.

Prior to comprehending this, I was among the crew making ‘jelpful’ suggestions that he would ‘reach more folks’ with graphs.

Just try to channel that energy of ‘gosh a picture would be so great’ into “here’s my chance to make a modest contribution in lieu of hitting the tip jar” .

Keep good track of that energy for the inevitable
‘garsh, why does it take so much effort to make a minimally-imperfect graph?’ moment-of-truth…

I think it IS okay to report difficulty in operating Cheifio’s ‘beer fund’ thingie.

Also, for new folks, the Langoliers seems to be a reference to an odd TV movie where the script called for wind, and the film crew obliged with large fans, but the clouds were unimpressed and remained stationary. The directorial shortcut was to add a line to the script; ~ ‘Around here, this kind of wind doesn’t move the clouds’,(?) or something to that effect.
Hmmm, that ‘explanation’ seems to fall a bit short of a convincing link to thermometer deletions. I understood it at one point, but my longer-lived higher cool mental records seem to have disappeared…

Good job of exposing the unscientific nature of the GHCN. Can I assume that all of these deletions are properly documented in peer-reviewed literature?

Could you or someone please make a list of information from NCDC, GISS and CRU that is still being withheld? I understand that CRU is still withholding their methodology, stations used and code, and maybe the same for GISS and NCDC. Thanks.

One thing I can enlighten on. Strasbourg Its the coldest city in France.

Aurillac is probably the coldest small town although the alpine town will be colder. Aurillac is in the Massif Centrale Strasbourg is on much lower ground and therefore relatively colder. Its in the east. Brrrrrrrrr very cold in winter and moderatly warm in summer.

So, no thermometers left for Czech Republic at all??
(Oldest temprature record -regular since 1775- in Central Europe is Praha-Klementinum record – incorporated by GHCN in the record of Praha/Ruzyne – or more exactly “connected” into international airport Praha-Ruzyne record – now also dropped?)
I was just looking at Giss and found maybe interesting things:
1. they state they produce their world anomaly maps from GHCN “Global Maps from GHCN Data” (http://data.giss.nasa.gov/gistemp/maps/)
2. I looked absolutely randomly in the 2009 data for the stations from your list at
(http://data.giss.nasa.gov/gistemp/station_data/)
and
NEW YORK CENTRAL PARK – data AUG-DEC missing
BARCELONA – data SEP-OCT missing
ILES GLORIEUS – data SEP-OCT missing
NALUT – all except JAN missing (very interesting record…)
CHIFENG – APR, SEP-DEC missing
KRMANSHAH – MAY, AUG, DEC missing (very interesting record too)
SAIKHAN-OVOO – APR missing
TERESINA – JUL, AUG, NOV, DEC missing (aso very interesting record)
LAMENTIN/MARTINIQUE/FT DE – SEP-NOV missing
etc. etc.
– one wonders how anybody on earth can – in the era of permanent manned space missions – use such incomplete records for global temperatures analysis?? …and now I’m fully convinced Hansen urgently needs a custody…
In all cases the Giss 2009 global anomaly is just a big nonsense or lie.
One hardly finds a station without some 2009 data missing. What this people at NOAA, NASA are doing? What for the taxpayers and others pay them? To have records even a high school student would be ashamed of? Unbelievable!!
This whole thing stinks like whole that prospering bunch of polar bears around the “mostly no thermometers at all” Arctica…

This is a posting of a comment I put up over at WUWT in the thread there that duplicates this article. It’s starting to look like the data constantly change and there is no particular ‘valid day’ when it’s ‘done’:

E.M.Smith (19:52:09) :
“Bob Koss (18:24:27) : I think you might want to keep your powder dry on the number of stations.”

So, no, I don’t see much reason to ‘keep power dry’.

Well, in thinking about this I decided that I was depending on NOAA:

1) Following their own statements.
2) Doing things that make sense and are consistent.
3) Following proper professional data set update standards.
4) Assuring that a broken (i.e. un-ripe) data set is not released.
5) Having a largely automated and standardized process for doing things.

Basically, I’m expecting professional standards of behaviour that may not be in evidence… The more I pondered, the more I realized there is little reason to expect any of those 5 behaviours given the things we have seen from CRU, GISS, GIStemp, et. al.

So I decided to go looking for the published “availability date” standard.

That paints a rather haphazard picture of the ‘update’ process. Given that haphazard method, I’ve decided there really IS NO reason to think that the GHCN data set is ever “done” or “ready”. It is just a “work in progress” and is a bit “slapdash” at any time…

First the figures are just for you – don’t pass on!!! I don’t normally see these. I just asked my MOHC contact – and he’s seen the furore on the blogs.

So about a year and 3 months ago. Probably still what happens…
These 3 paras (below) are from the GHCN web site. They appear to be the only mention I can see of the WMO CLIMAT network on a web site.

I could not find much on the web site either, but perhaps searching with chunks of the material you are looking for as keys would find it? Something to explore later…
The rigorous QC that is being talked about is done in retrospect.
They don’t do much in real time – except an outlier check.

OK, so QC is sort of an after the fact glue on… and the web pages are doing some sellers puff about “rigor”.
Anyway – the CLIMAT network is part of the GTS. The members (NMSs) send their monthly averages/total around the other NMSs on the 4th and the 18-20th of the month afterwards.

So, by the 8th the data set OUGHT to have been complete. But it comes around again on the 18-20th. That means we might get the data ‘fixed’ next week… but maybe by the end of the month for sure?
Few seem to adhere to these dates much these days, but the aim is to send the data around twice in the following month.

Or NOT…
Data comes in code like everything else on the GTS, so a few centres (probably a handful, NOAA/CPC, MOHC, MeteoFrance, DWD, Roshydromet, CMA, JMA and the Australians) that are doing analyses for weather forecasts have the software to pick out the CLIMAT data and put it somewhere.

“put it somewhere”… that’s comforting…
At the same time these same centres are taking the synop data off the system and summing it to months – producing flags of how much was missing. At the MOHC they compare the CLIMAT message with the monthly calculated average/total. If they are close they accept the CLIMAT. Some countries don’t use the mean of max and min (which the synops provide) to calculate the mean, so it is important to use the CLIMAT as this is likely to ensure continuity.

“how much is missing” flags… “If they are close”… now there is a fine standard metric of acceptable error band. /sarcoff>
If they don’t agree they check the flags and there needs to be a bit of human intervention. The figures are examples for this October. What often happens is that countries send out the same data for the following month.

“a bit of human intervention”… “often happens” “same data”. So, we have no idea if there are loads of bad data that was just broken in both the dailies and the monthlies (if they are both broken by, oh, reading a sign wrong on the dailies, it will just sail through?!) or if blocks are just repeated because what ‘often happens’ happens? And we have no idea what a “bit of human intervention” is, or if there are standards for it or for how long it might take. AND we wait until the 18-20th to get a second bite at the apple and hope one of them works out to be right…
This happens mostly in developing countries, as a few haven’t yet got software to produce the CLIMAT data in the correct format. There is WMO software to produce these from a wide variety of possible formats the countries might be using. Some seem to do this by overwriting the files from the previous month. They add in the correct data, but then forget to save the revised file. Canada did this a few years ago – but they sent the correct data around a day later and again the second time, after they got told by someone at MOHC.

So if someone notices a really bad screw up, they can get the data sent around again. Sort of whenever. In some format or other. Possibly overwritten with old values, if they remembered to save the file..
My guess here is that NOAA didn’t screw up, but that Russia did. For all countries except Russia, all data for that country comes out together. For Russia it comes out in regions – well it is a big place! Trying to prove this would need some Russian help – Pasha Groisman? – but there isn’t much point. The fact that all the affected data were from one Russian region suggests to me it was that region.
Probably not of much use to an FAQ!
Cheers
Phil

And some chunks of countries could get screwed up too, but hey, it’s not like you can find out if it was screwed up or when or by whom or whatever “there isn’t much point”… so why bother. It’s only data…

There are then three paragraphs of ’sellers puff’ quoted from the web site that looks oddly familiar, vapid, and empty.

Then the Gavin statement to which the reply was sent, and the original complaint from Jones that caused Gavin’s reply. I’ll just include Gavin’s bit:
At 12:56 17/11/2008, you wrote:

thanks.
Actually, I don’t think that many people have any idea how the NWS’s
send out data, what data they send out, what they don’t and how these
things are collated. Perhaps you’d like to send me some notes on this
that I could write up as a FAQ? Won’t change anything much, but it
would be a handy reference….
gavin

Where we find out that not “many people have any idea” how it all works and they didn’t even have a FAQ about it but could use one as ‘a handy reference’.

Well, with those kinds of “standards” and “procedures” and “documentation” I find I must now recant my statement that I didn’t see much reason to “keep my powder dry”.

It would seem that there is no way to ever know what stations are in, what stations are out, WHEN they are in, and WHEN they are out, or even if what is IN is really what is supposed to be in. As long as it’s self consistent between the dailies and monthlies, it can be in, unless it’s a very bad “outlier” (but even that might be an error if we are going to be having “extreme weather events” as they ought to be, by definition, outliers…)

At any rate, given their “process”, it looks like we can have some confidence that the glue-on QA will be done eventually and most of the time a lot of the data will be available by the middle of the following month, except when it’s the end of the month, or maybe the next month… or whatever.

For Europe – using database-derived trends for GISS adjusted data:
65 of the 70 dropped stations were in the database – the 5 missing ones may be below the QC threshold

Average trend for ALL European Stations: 0.56 Deg C/Century warming
Average trend for Dropped European Stations: 1.23 Deg C/Century warming
Average trend for Remaining European Stations: 0.5 Deg C/Century warming

So for Europe at least, dropping these thermometers MAY not have a warming effect. However, these trends are for all years – it depends on the forward trends at the remaining stations.

So at this point, it looks like when NOAA make a file available, it’s only a suggestion not an endorsement, and it will be constantly changed, day by day, for months. Given that each month there will be a new month of data: any single day the image of the file may well be different from any other day but with a ‘spike’ of newness near the 2nd week of the month.

How does one assure any consistency of processing or comparison of results between researchers when the base data is constantly mutated? How does one “measure” the process or even standardize the error detection? Unless you know the DAY that GISS chooses to do their copy of the GHCN input, you can not duplicate their results (though you could come “close” most of the time.) But given the tendency to ‘spread’ a value 1200 km in GIStemp, that single December added value could influence a box of space 2400 km x 2400 km and that could also shift hemispheric values, etc.

Would be happy to make graphs (they don’t take me much time, been doing them way too long). What I need is tab delimited data so I can ingest easily and with quality into Excel. You should have seen the steps I went through on these graphs for Chiefio:

Hey, I know what you mean about the steps to get a clean ingestion. (!) (some only found after publishing something erroneous, in my case)…

I eventually decided that the best Excel path was to import as ‘fixed columns’. The only trick there is that Excel default column suggestion is based on the rows visible within the little preview window, and if there are no 2 digit negative numbers in that window, it will pick a column divider that omits the – sign when a 2digit negative number inevitably appears.

One other refinement was to define the station number column as a fixed point number with nothing to the right of the decimal; otherwise they come through as scientific notation (assuming you wish to retain this ~’metadata’ with your analysis for point-checks. I was trying to make geo-referenced plots).

One last point; as you probably learned, the wide variation of entries in the station name column is the downfall of any “**” delimited import method, regardless of your choice of “**”.
So, perhaps you already figured this out.

I think some of the challenge for el Jefe is the limitations of publishing the tables through WP. But the above methods are relatively simply implemented. Maybe even ‘automated’ through XL tools?

But all this stations, amazingly, have in the GISS database listed the annual means for 2009:
MILESOVKA – 6.28
PRAHA/RUZYNE – 8.75
PRAHA-LIBUS – 9.48
BRNO/TURANY – 9.83
OSTRAVA/MOSNO – 9.21
How one counts the annual mean when the one have data of 4 months missing from the year??

sorry last try: (most probably there’s a problem with the google satelite maps link)
…the thermometer station is 200m from runway (here I don’t link the google maps) so one would expect not the adjustments down in the early years – when there were just couple of prop-planes starting a day, but the opposite: adjustments down in the recent years for AHI (airport heat island) because now there a jet plane starts or lands (with its ~200MW thermal output – if I count it well – an equivalent of >1km2 mean surface thermal solar input at 50°N + and loads of CO2) every couple of minutes.

I also cut and paste then import to produce Excel charts. I cut the data from Chiefio’s page, paste that into Word, save it as plain text, then import that plain text file into Excel as space delimited (not tab delimited). Then can do the Excel manipulations to obtain columns for charts. Very few problems having to manually align data.

Re your comparison between GISS and CHMU data. I have come across this type of mismatch again and again – I think Canada had to be the worst – so much difference that I wondered if I had the same station (the WMO numbers matched).

I looked at 40+ stations for TonyB (who also comments here), comparing data from GISS, Rimfrost (http://www.rimfrost.no/ – which uses GISS and/or national sources) and national sources where possible. Often the differences between them were very small, but a large number of points altered in this way.

At the moment I am looking at the GHCN and GISS ajustments to the GHCN ‘raw’ (unadjusted) data. They are very different too.

I was also interested in what you said earlier about missing data (February 12, 2010 at 3:58 pm) as I am preparing a post about that on my own blog. GISS and GHCN calculate the annual means via seasonal means:

DJF – MAM – JJA – SON : four seasonal means averaged to a yearly mean. If one month is missing, sometimes the seasonal mean is just average of the two remaining, but sometimes, it has an ‘assumed’ value for the missing month, for which I have found a few examples where they are clearly wrong (warmer than a typical month). I’ll have a look at the examples you list.

Yes, there are unbelievable gaps in the data, and not just 2009.
The classical example is the station Praha Klementinum – one of the oldest instrumental temperature record in the world (and thus extremely valuable) having uninterupted measurements since 1770 -courtesy of an old monastery – till present.
And what the “scientists” from GHCN, GISS and CRU did with this valuable record? They raped the record cuting the pre-1850 (or GISS even the pre-1880) values, then cut it again in 1940, leaving the whole 40ties out and then “connecting” to it another station of Praha/Ruzyne beginning 1949 (even the Praha-Klementinum is still there and measuring) never mentioning the pre-1940 data are from different station -not Praha/Ruzyne but Praha Klementinum.
Go here http://data.giss.nasa.gov/gistemp/station_data/ – search for “Praha/Ruzyne” ,
the real Praha/Klementinum data -going back 1770 to the 2009 I’ve here: http://xmarinx.sweb.cz//KLEMENTINUM.xls
You can compare…Mind the warm decade 1790-1799.
I would just add that the present UHI of Praha Klementinum is now by me estimated (using comparison with other non-urban stations around) >0.44°C and thus the warming during last 200 years in Prague was <0,25°C/century!!! – I don't know if there is a "catastrophic warming around the globe", but in Prague, central Europe, surely not.

BUT what is maybe even more interesting -is the thing which one can call "PHANTOM DATA"
– I'll explain: Courtesy of the Phil Jones declaring the CRU raw data "missing" the CRU subsequently contacted among numerous others the Czech Met Office (CHMI) to send them the Czech raw data again. The Czech Met Office climatologist then was ?clever or stupid? enough and so he asked CRU whether the CRU can send them (to CHMI) the data they (in CRU) have. So the CRU (amazingly) did (even before they declared the data "missing", "lost during moving"…)
Then, because I and others wrote in Czech some popular articles about the global temperature data manipulation in NOAA, GISS – sourcing information from Chiefio, WUWT and ICECAP.
The Czech Met Office climatologists then hastily published counter-articles, trying to prove the CRU didn't manipulate the Czech data.
But with their articles they also published the Czech data the CRU have sent to them together with the data the Czech Met Office has.
you can download here: http://tinyurl.com/y96e7fh -and see the differences.
And now it comes: I was then looking to the CRU data published by CHMI and immediately discovered they have there some data for stations Cheb, Brno/Turany, Ostrava/Mosno from 50ties -data which even the Czech Met Office (CHMI) doesn't have (for their own stations). Whole decade of data! Subsequently I discovered even more amazing thing: the CRU has the 1953 data for the station Cheb -even for the PERIOD BEFORE THE STATION WAS EVEN FOUND! (late 1954) Subsequently, of course, I asked the Czech Met Office climatologist (I sometimes discuss with him at the internet) where the data come from. After some pressure in public forums he confessed the Czech Met Office has ABSOLUTELY NO IDEA where the data come from. The climatologist promised me to find out what's going on, so far – even after several weeks – no word from him.
So that's why I call it the "phantom data".
And I decided to publish the whole story.
Did CRU fabricated the data? Are there more fabricated data in their files? Is it the reason why the CRU doesn't want to publish the data and declared them "missing"? (to avoid selfincrimination?) Or the data come originally from NOAA/GHCN and were fabricated there and CRU just tryies to whitewash the whole thing?
I would think this question maybe should be posed at the British parliament inquiry into Climategate….

(11:08 am vjones)
Thanks a lot for looking into the issue.
Yes, the treatment of the data for Praha/Klementinum/Ruzyne is different. But always wrong.
What I think is that this stations shouldn’t be connected in one dataset at first place. -Because the Praha/Klementinum station is just in the very very center of Prague, at the elevation of 192m above sea level, in immediate vicinity of the river, surrounded by the always heated buildings -the Klementinum -the old jesuit convent- is now Czech National and University Library (I remember long days spent in the study there)
googlemaps:http://tinyurl.com/yffvz9c
In contrast Praha/Ruzyne has elevation of 380m and the station is just <200m from the widely used runway 31 at the Prague internationl airport.http://tinyurl.com/yknfy36
That's why the record is so different before and after 1940ies, with the large step – and the raw data then look like there is such a cooling between 1939-1949 during that "missing data decade".
200m elevation difference itself would make ~1.8°C in the continental climate…
But the adjustments made by GHCN are absolutely inappropriate. Instead of elevation homogenization, they adjust the 50ties-90ties DOWN -that's the baseline for the global anomaly! – instead of doing the opposite – correcting 90ties-2009 DOWN, because of the Airport Heat Island. They shouldn't adjust 50ties-90ties at all – because in that time there were not much airplanes taking-off at the time by fields surrounded airstrip in a communist country – in contrast with today when the airport is very frequented, recently twice enlarged for the ever rising low-budget flights.
The connection of Klementinum with Ruzyne is methodological nonsense. But they did it probably because 1. the Ruzyne data are easily available through ICAO airtraffic meteorological network and also 2. because it introduces a confusion – in fact the pre-1950 data are irelevant, because anyway they don't use them for the 1951-1980 default AGW baseline.
In fact you can find the warming trend at Klementinum record itself. But if you consider estimated UHI and compare the decade 2000-2009 with the decade 1790-1799, then you en up with just <0.25°C/century. But what the warmistas want? They want steep warming in last 60 years. -So they cut pre-1880 values, adjust 50ties-90ties ~2°C DOWN, put it in the baseline, then compare baseline with present …and here you have the warming.
They probably thought nobody will ever scrutinize it. They probably do this elsewhere too – there is the notorious example of Darwin in Australia and probably more…

Back to the phantom data.
Here:http://tinyurl.com/yl6k2h2
you can see the GHCN has even the Cheb (the place where duke of Wallenstein was murdered) data going sparsely back to even 1950 – although the station was even according to the Czech Met Office publicly available record found in 1954:http://www.chmi.cz/meteo/opss/stanice.php?ukazatel=cheb
So where they got the incomplete data in NOAA/GHCN for 1950-1953 and subsequently the CRU for whole 1953 – if even the Czech Met office itself has the data only since december 1954?

I would like to make a blog post about this not only at my Czech blog, but in english – although I'm not very well at it, so I would need probably some proofreading :), because somebody should I think pose this question at the british parliamentary inquiry – as it fits in their question 3: ""to what extent the CRU and GHCN and GISS datasets are independent"" – which I think largely aren't and even the "phantom data" look like they mostly overlap and come originally from GHCN datasets.
And anyway -There are so many met stations in Czech Republic, so why the GHCN, CRU, GISS always chose the same, mostly airports, even "engrafting" the record at one of the most worlds unique instrumental record as the Klementinum record undoubtedly is?…

I just finished an explanatory paper about the Klementinum record, history, details of the UHI and Local Warming estimation to 0.25°C/century and sent it to the climategate.com as an addendum to the Mr. O’Sullivan’s article.
The working draft pdf copy with pictures can be found here:http://xmarinx.sweb.cz//KlementinumUHI.pdf
enjoy!

where the UHI effect, details about the history of this station and comparisons with neighboring stations including Ruzyne are described.

In the conclusions they say:

“In a previous paper by Brazdil (1993), the warming at
the Klementinum due to the intensification of the UHI
was estimated to be 0.07-0.083C/10 yr from the beginning
of the century up to about 1940, increasing afterwards
to about 0.13C/10 yr.
In this paper, the values of
warming are somewhat lower. Higher values of warming
following from the comparison with Milesovka can be
explained by the fact that at mountain stations, the linear
upward trend of air temperature in our century is lower
than at stations at lower elevations.”

However, the UHI effect ceased to rise in the first half of 60’s and does not change since then.

And about the homogenizations – surely they can be done, but with care and looking at the metadata to find the sources of inhomogenities, not slapping a universal algorithm over the whole globe!
Here’s a short article about homogenizations in Czech Rep. – it’s a shortened version of the PhD Thesis.

“Adjustment was made for those temperature series in which years of statistically significant inhomogeneities, as indicated by the tests, were clearly related to station metadata (such as relocation).
Metadata, however, seldom include all the changes taking place at a given station. So adjustment was also carried out for cases of clearly “undoubted” inhomogeneities, which, although not evident in the metadata, were unambiguously indicated by the results of tests and were physically justified (see Brázdil and Stepánek, 1998)”

Yeah I know the Brazdils article. Unfortunately he does the comparisons with a bit far and different elevation staions using classical approach. Anyway his figures combined through 20th century and the first decade of 21st century in fact imply very simmilar results as I came to. The issue is the paper doesn’t yet cover the 2000ies, so he doesn’t for example see the additional ~0.05°C/decade UHI surge again recently.
In fact my figure 0.49°C of overall Klementinum UHI bias against the state in 1790ies is still rather a quite conservative estimation made nevertheless using different, more heuristic datamining approach, because I don’t much care about the trends, what is important to me is just the total bias at present for the temperature comparison with relatively very far past, which is what really counts in climatological sense. So we maybe differ in exact figure, but the order of the magnitude of several tenths of centigrade is the same and again that’s what really counts, when we talk about several tenths of centigrade/century of possible temperature surge in climatological sense, because the real climate from logic depends on absolute temperature values, not the even best way estimated trends.
If I would go deeper and robustly estimate also the Ruzyne AHI bias I’m almost sure, that I would come to the figures of “no climatologically significant warming in last 200 years at all” in Prague, which would staunchly bury any credibility of the CAGW scam – at least for the people who are able to understand what it is all about. But I leave this for now to others who are paid for making such analyses.

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